fix training.py

megatron/training.py

@@ -141,7 +141,6 @@ def pretrain(train_valid_test_dataset_provider,
     print_rank_0('training ...')
 
     iteration = 0
-    # if not args.run_dialog:
     if args.do_train and args.train_iters > 0:
         iteration = train(forward_step_func,
                           model, optimizer, lr_scheduler,
@@ -163,7 +162,7 @@ def pretrain(train_valid_test_dataset_provider,
         evaluate_and_print_results(prefix, forward_step_func,
                                    test_data_iterator, model,
                                    0, True)
-  
+
 def update_train_iters(args):
 
     # For iteration-based training, we don't need to do anything
@@ -355,8 +354,6 @@ def setup_model_and_optimizer(model_provider_func, model_type):
         torch.distributed.barrier()
         timers('load-checkpoint').start()
         args.iteration = load_checkpoint(model, optimizer, lr_scheduler)
-        # need to set train_samples to None
-        args.train_samples = None
         torch.distributed.barrier()
         timers('load-checkpoint').stop()
         timers.log(['load-checkpoint'])
@@ -662,9 +659,7 @@ def train(forward_step_func, model, optimizer, lr_scheduler,
     # Iterations.
     iteration = args.iteration
 
-    # if not args.run_dialog:
     timers('interval-time').start()
-
     print_datetime('before the start of training step')
     report_memory_flag = True
     while iteration < args.train_iters:
@@ -860,7 +855,7 @@ def build_train_valid_test_data_iterators(
         else:
             train_samples = args.train_iters * args.global_batch_size
         eval_iters = (args.train_iters // args.eval_interval + 1) * \
-                      args.eval_iters
+                     args.eval_iters
         test_iters = args.eval_iters
         train_val_test_num_samples = [train_samples,
                                       eval_iters * args.global_batch_size,